Ex-Amazon Tech Lead Explains Big O

https://codecrack.org - Check out my all-in-one coding interview prep platform. Big O notation helps you explain time and space complexity in a coding interview. Start with what the input size counts, then look at how the work grows. A simple array sum leads into linear growth. Direct indexed access shows constant work. Nested loops build a comparison grid, while a fixed inner loop shows why nested code does not always mean quadratic time. From there, separate phases and fixed multipliers explain how to simplify a complexity estimate. Sorted search introduces repeated halving and logarithmic growth. The final section compares a few reused variables with a temporary array copy to explain auxiliary space. The examples cover O(1), O(n), O(n squared) and O(log n), by tracing what changes as the input gets larger. #codinginterview #algorithms #programming #leetcode